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About This Role
About Acadia Pharmaceuticals
Acadia is committed to turning scientific promise into meaningful innovation that makes the difference for underserved neurological and rare disease communities around the world. Our commercial portfolio includes the first and only FDA\-approved treatments for Parkinson's disease psychosis and Rett syndrome. We are developing the next wave of therapeutic advancements with a robust and diverse pipeline that includes mid\- to late\-stage programs in Alzheimer's disease psychosis and Lewy body dementia psychosis, along with earlier\-stage programs that address other underserved patient needs. At Acadia, we're here to be their difference.### Please note that this position is based in San Diego, CA, South San Francisco, CA, or Princeton, NJ. Acadia's hybrid model requires this role to work in our office three days per week on average.
Position Summary
The Associate Director, AI/ML Engineering serves as a hands\-on technical leader driving the design, architecture, and delivery of Generative AI and agentic AI solutions across the enterprise. This role builds scalable multi\-agent systems, connects AI solutions to enterprise data and tools, and ensures safe, reliable deployment through robust evaluation and guardrail frameworks. The position also applies strong machine learning and foundation model expertise to deliver high\-impact use cases within a regulated biopharmaceutical environment.
Primary Responsibilities
- Design, build, and deploy agentic AI workflows that automate and transform complex business processes, leveraging multi\-agent orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI, or equivalent).
- Architect and implement MCP servers to expose enterprise tools, APIs, and data sources as standardized capabilities consumable by AI agents.
- Connect multi\-agent systems to enterprise databases, internal APIs, and MCP servers to enable grounded, context\-aware, and action\-oriented AI solutions.
- Partner cross\-functionally with internal teams to define data contracts, lineage standards, and quality thresholds required for AI/ML use cases.
- Design and implement agentic memory systems (short\-term, long\-term, episodic) and planning/reasoning loops to support reliable autonomous task execution.
- Evaluate agentic system performance across accuracy, reliability, latency, cost, and safety dimensions using structured benchmarks and red\-teaming methodologies.
- Build and maintain guardrail frameworks (input/output filtering, content moderation, policy enforcement, hallucination detection) to ensure the safety, compliance, and trustworthiness of GenAI and agentic solutions.
- Develop retrieval\-augmented generation (RAG) pipelines, including chunking strategies, embedding models, vector store selection, and retrieval optimization for enterprise knowledge bases.
- Apply prompt engineering, few\-shot learning, and fine\-tuning techniques to adapt foundation models for domain\-specific pharma use cases.
- Design, develop, validate, and deploy traditional machine learning models (classification, regression, clustering, time\-series, survival analysis) to address structured business problems.
- Build and maintain end\-to\-end ML pipelines adhering to LLM Ops / ML Ops standards including model registry, evaluation benchmarks, prompt/version control, observability, and rollback procedures.
- Experience in working with real\-world data (RWD), claims data, EHR data, Clinical Study data, translational and biological data and the corresponding databases is a plus.
- Other responsibilities as assigned.
Education/Experience/Skills
- Master's or PhD in Machine Learning, Computer Science, Data Science, Information Systems, or a related quantitative discipline
- Minimum of 7 years of experience in AI/ML engineering, including at least 3 years of hands\-on experience with Generative AI and agentic AI systems
- Expertise in multi\-agent frameworks such as LangGraph, AutoGen, CrewAI, Semantic Kernel, or similar technologies
- Experience building MCP servers and integrating AI systems with enterprise data sources, APIs, and tools
- Strong experience in RAG pipeline development, embedding models, and vector database technologies
- Proficiency in Python and machine learning frameworks such as PyTorch, TensorFlow, scikit\-learn, and Hugging Face
- Experience implementing ML Ops or LLM Ops practices, including model lifecycle management, evaluation, and deployment
- Ability to travel domestically and internationally as required
Physical Requirements
This role involves regular standing, walking, sitting, and the use of hands for handling or operating equipment. The employee may also need to reach, climb, balance, stoop, kneel, crouch, and maintain visual, verbal, and auditory communication in a standard office environment and while working independently from remote locations. The employee must occasionally lift and/or move up to 20 pounds. This position requires the ability to travel independently overnight and/or work after hours as required by travel schedules or business needs.
\#LI\-HYBRID
\#LI\-CS1
What we offer US\-based Employees:
- Competitive base, bonus, new hire and ongoing equity packages
- Medical, dental, and vision insurance
- Employer\-paid life, disability, business travel and EAP coverage
- 401(k) Plan with a fully vested company match 1:1 up to 5%
- Employee Stock Purchase Plan with a 2\-year purchase price lock\-in
- 15\+ vacation days
- 13 \-15 paid holidays, including office closure between December 24th and January 1st
- 10 days of paid sick time
- Paid parental leave benefit
- Tuition assistance
EEO Statement (US\-based Employees): Studies have shown that women and people of color are less likely to apply for jobs unless they believe they meet every one of the qualifications in the exact way they are described in job postings. We are committed to building a diverse, equitable, inclusive, and innovative company, and we are looking for the BEST candidate for the job. That candidate may be one who comes from a less traditional background or may meet the qualifications in a different way. We strongly encourage you to apply, especially if the reason you are the best candidate isn't exactly what we describe here.
It is the policy of Acadia to provide equal employment opportunities to all employees and employment applicants without regard to considerations of race, including related to hairstyle, color, religion or religious creed, sexual orientation, gender, gender identity, gender expression, gender transition, country of origin, ancestry, citizenship, age, physical or mental disability, genetic information, legally\-protected medical condition or information, marital status, domestic partner status, family care status, military caregiver status, veteran or military status (including reserve status, National Guard status, and military service or obligation), status as a victim of domestic violence, sexual assault or stalking, enrollment in a public assistance program, or any basis protected under federal, state or local law.
As an equal opportunity employer, Acadia is committed to a diverse workforce. If you are a qualified individual with a disability or a disabled veteran, you have the right to request a reasonable accommodation. Furthermore, you may request additional support if you are unable or limited in your ability to use or access Acadia's career website due to your disability, along with any accommodations throughout the interview process. To request or inquire about your reasonable accommodation, please complete our Reasonable Accommodation Request Form or contact us at talentacquisition@acadia\-pharm.com or 858\-261\-2923\.
Please note that reasonable accommodations granted throughout the recruiting process are not guaranteed to be the same accommodations given if hired. A new request will need to be submitted for any ADA accommodations after starting employment.
California Applicants: Please see Additional Information for California Residents within our Privacy Policy.
Canadian Applicants: Please see Additional Information for Canadian Residents within our Privacy Policy.
Applicants in the European Economic Area, Switzerland, the United Kingdom, and Serbia: Please see Additional Information for Individuals in the European Economic Area, Switzerland, the United Kingdom, and Serbia within our Privacy Policy.
Notice to Search Firms/Third\-Party Recruitment Agencies (Recruiters): The Talent Acquisition team manages the recruitment and employment process for Acadia Pharmaceuticals Inc. ("Acadia"). Acadia does not accept resumes from recruiters or search firms without an executed search agreement in place. Resumes sent to Acadia employees in the absence of an executed search agreement will not obligate Acadia in any way with respect to the future employment of those individuals or potential remuneration to any recruiter or search firm. Candidates should never be submitted directly to our hiring managers or employees.
Salary Context
This $159K-$199K range is below the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Acadia Pharmaceuticals, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $178,940 based on 11,900 positions with disclosed compensation. Director-level AI roles across all categories have a median of $243,000. Disclosed range: $159K to $199K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Acadia Pharmaceuticals AI Hiring
Acadia Pharmaceuticals has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Princeton, NJ, US. Compensation range: $167K - $199K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (119) are outnumbered by mid-level (1,813) and senior (1,472) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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